Exponential Families and Maximum Entropy

[latexpage]An exponential family parametrized by $\boldsymbol\theta \in \mathbb R^d$ is the set of probability distributions that can be expressed as

\[ p({\bf x} \,|\, \boldsymbol\theta) =\frac{1}{Z(\boldsymbol\theta)} h({\bf x}) \exp\left( \boldsymbol\theta^{\mathsf T}\boldsymbol\phi({\bf x}) \right) ,\]

for given functions $Z(\boldsymbol\theta)}$ (the partition function), $h({\bf x})$, and $\boldsymbol\phi({\bf x})$ (the vector of sufficient statistics). Exponential families can be discrete or continuous, and examples include Gaussian distributions, Poisson distributions, and gamma distributions. Exponential families have a number of desirable properties. For instance, they have conjugate priors and they can summarize arbitrary amounts of data using a fixed-size vector of sufficient statistics. But in addition to their convenience, their use is theoretically justified. Continue reading “Exponential Families and Maximum Entropy”